An alternative correlation statistic is the Intraclass Correlation Coefficient (ICC). This is commonly used to compare observers (for inter-observer correlation/reliability) or sets of measurements for one observer (for intra-observer correlation/reliability). Suppose that an observer makes a series of measurements on MRI scans. He or she then repeats the measurement after a period of time and we want to quantify the intra-observer reliability. The data set MRI.rda contains 62 measurement pairs. Load it into the Data Viewer to proceed.
A Pearson’s correlation coefficient can be calculated as described:
cor(MRI$FirstMeasurement,MRI$SecondMeasurement,method='pearson')
[1] 0.9962104For the ICC, the package “irr”1 should be installed:
library(irr)And call the icc funtion to calculate the ICC:
icc(MRI,model="twoway",type="agreement")
Single Score Intraclass Correlation
Model: twoway
Type : agreement
Subjects = 60
Raters = 2
ICC(A,1) = 0.996
F-Test, H0: r0 = 0 ; H1: r0 > 0
F(59,54.9) = 525 , p = 1.93e-60
95%-Confidence Interval for ICC Population Values:
0.993 < ICC < 0.998The interclass correlation coefficient is 99.6% and the p-value for the test of no association is significant, confirming there is an association.